🌾 Sentinel-1 Soil Moisture Estimation

Estimate field-scale soil moisture from Sentinel-1 SAR data using Google Earth Engine, R, and QGIS over rainfed agriculture practices. This project focuses on providing farmers and researchers with tools to monitor soil moisture levels effectively, helping improve agricultural practices and decision-making.

Table of Contents

• Introduction
• Features
• Technologies Used
• Installation
• Usage
• Workflow
• Contributing
• License
• Contact
• Releases

Introduction

Soil moisture is crucial for agriculture. It affects crop growth, yield, and water management. This repository provides a framework to estimate soil moisture using Sentinel-1 Synthetic Aperture Radar (SAR) data. By leveraging Google Earth Engine, R, and QGIS, users can analyze and visualize soil moisture levels efficiently.

Features

• Field-scale Estimates: Provides accurate soil moisture estimates at the field level.
• Open Science: Promotes transparency and reproducibility in agricultural monitoring.
• Multi-Platform Support: Compatible with Google Earth Engine, R, and QGIS.
• User-Friendly: Simple scripts and workflows for ease of use.
• Comprehensive Documentation: Guides for installation, usage, and troubleshooting.

Technologies Used

• Sentinel-1 SAR Data: High-resolution radar data for soil moisture estimation.
• Google Earth Engine (GEE): A powerful platform for geospatial analysis.
• R: A programming language for statistical computing and graphics.
• QGIS: A free and open-source geographic information system.

Installation

To get started, follow these steps:

1. Clone the Repository: bash git clone https://github.com/BoogyMan-bot/sentinel-1-soil-moisture.git

2. Install Required Software:

   • Ensure you have R and QGIS installed on your machine.
   • Install the necessary R packages. You can find the list of packages in the requirements.txt file.

3. Set Up Google Earth Engine:

   • Sign up for Google Earth Engine.
   • Authenticate your account by following the instructions in the GEE documentation.

Usage

Once the installation is complete, you can start using the scripts provided in this repository.

1. Run the GEE Script:

   • Navigate to the gee_scripts directory.
   • Open the script and modify the parameters as needed.
   • Execute the script in the GEE Code Editor.

2. Process Data in R:

   • Load the SAR data and perform statistical analysis.
   • Use the provided R scripts for soil moisture estimation.

3. Visualize in QGIS:

   • Import the processed data into QGIS.
   • Use the visualization tools to analyze soil moisture patterns.

Workflow

The workflow for estimating soil moisture involves several steps:

1. Data Acquisition:

   • Download Sentinel-1 SAR data for the area of interest.

2. Data Preprocessing:

   • Clean and preprocess the data using GEE scripts.

3. Soil Moisture Estimation:

   • Use R scripts to estimate soil moisture based on the processed data.

4. Visualization:

   • Visualize the results in QGIS to identify trends and patterns.

5. Analysis and Reporting:

   • Generate reports based on the findings to support decision-making.

Contributing

Contributions are welcome! If you want to improve this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature/YourFeature).
6. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any inquiries or suggestions, feel free to reach out:

• Email: your.email@example.com
• GitHub: BoogyMan-bot

Releases

You can find the latest releases here. Download the necessary files and execute them to start your soil moisture estimation journey.

Thank you for your interest in the Sentinel-1 Soil Moisture Estimation project! Your support helps promote better agricultural practices and sustainable farming.